A cost-effective plan for global testing - An infection rate stratified, algorith guided, multiple-level, continuously pooled testing strategy

Tianshu Gu, Lan Yao, Xia Meng, J. Carolyn Graff, Donald Thomason, Jing Li, Wei Dong, Yan Jiao, Lotfi Aleya, Marcello Maida, Cong Yi Wang, Barbara Zangerl, Sem Genini, Kunal Ray, Emanuel Goldman, Jiafu Ji, Andrei V. Alexandrov, Dianjun Sun, Weikuan Gu, Yongjun Wang

Research output: Contribution to journalArticlepeer-review

Abstract

The most effective measure to prevent or stop the spread of infectious diseases is the early identification and isolation of infected individuals through comprehensive screening. At present, in the COVID-19 pandemic, such screening is often limited to isolated regions as determined by local governments. Screening of potentially infectious individuals should be conducted through coordinated national or global unified actions. Our current research focuses on using resources to conduct comprehensive national and regional regular testing with a risk rate based, algorithmic guided, multiple-level, pooled testing strategy. Here, combining methodologies with mathematical logistic models, we present an analytic procedure of an overall plan for coordinating state, national, or global testing. The proposed plan includes three parts 1) organization, resource allocation, and distribution; 2) screening based on different risk levels and business types; and 3) algorithm guided, multiple level, continuously screening the entire population in a region. This strategy will overcome the false positive and negative results in the polymerase chain reaction (PCR) test and missing samples during initial tests. Based on our proposed protocol, the population screening of 300,000,000 in the US can be done weekly with between 15,000,000 and 6,000,000 test kits. The strategy can be used for population screening for current COVID-19 and any future severe infectious disease when drugs or vaccines are not available.

Original languageEnglish (US)
Article number144251
JournalScience of the Total Environment
Volume765
DOIs
StatePublished - Apr 15 2021

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

Keywords

  • Coronavirus
  • COVID-19
  • Infection
  • Population
  • Screening
  • Test

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